import math
import pandas as pd
import random
import re
import string
import torch
import torch.distributed as dist
import torchvision.transforms as T
import transformers
import warnings
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoTokenizer, AutoConfig, AutoModel, CLIPImageProcessor

from ..base import BaseModel
from ...dataset import DATASET_TYPE, DATASET_MODALITY
from ...smp import *

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose(
        [
            T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
            T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
            T.ToTensor(),
            T.Normalize(mean=MEAN, std=STD),
        ]
    )
    return transform


def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float("inf")
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio


def dynamic_preprocess(
    image, min_num=1, max_num=6, image_size=448, use_thumbnail=False
):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j)
        for n in range(min_num, max_num + 1)
        for i in range(1, n + 1)
        for j in range(1, n + 1)
        if i * j <= max_num and i * j >= min_num
    )
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size
    )

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size,
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images


def load_image(image_file, input_size=448, max_num=6, upscale=False):
    image = Image.open(image_file).convert("RGB")
    if upscale:
        image = image.resize((image.width * 2, image.height * 2), Image.BILINEAR)
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(
        image, image_size=input_size, use_thumbnail=True, max_num=max_num
    )
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values


def get_local_rank_and_local_world_size():
    if not dist.is_available():
        return 0, 1
    if not dist.is_initialized():
        return 0, 1

    if "SLURM_LOCALID" in os.environ:
        local_rank = int(os.environ["SLURM_LOCALID"])
        local_world_size = int(os.environ["SLURM_NTASKS_PER_NODE"])
        return local_rank, local_world_size

    if "LOCAL_RANK" in os.environ and "LOCAL_WORLD_SIZE" in os.environ:
        return int(os.environ["LOCAL_RANK"]), int(os.environ["LOCAL_WORLD_SIZE"])

    raise NotImplementedError(
        "Fail to get local_rank and local_world_size! "
        "Please ensure that you set the environment variable "
        "`LOCAL_RANK` and `LOCAL_WORLD_SIZE`"
    )


def split_model(model_path):
    num_gpus_per_node = torch.cuda.device_count()
    rank, world_size = get_rank_and_world_size()
    try:
        local_rank, local_world_size = get_local_rank_and_local_world_size()
    except:
        local_rank = rank

    if "GPUS_PER_PROCESS" in os.environ:
        gpus_per_process = int(os.environ["GPUS_PER_PROCESS"])
    else:
        gpus_per_process = 8  # default to use 8 GPUs for one model
    gpus_per_process = min(gpus_per_process, num_gpus_per_node // local_world_size)
    start_gpu = local_rank * gpus_per_process
    end_gpu = start_gpu + gpus_per_process

    assert end_gpu <= num_gpus_per_node, (
        f"Process {local_rank} tries to access GPU {end_gpu}, "
        f"but only {num_gpus_per_node} GPUs are available per node."
    )

    visible_devices = list(range(start_gpu, end_gpu))

    device_map = {}
    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)

    num_gpus_for_vit = 0.5
    num_layers = config.llm_config.num_hidden_layers
    num_layers_per_gpu = math.ceil(
        num_layers / (len(visible_devices) - num_gpus_for_vit)
    )
    num_layers_per_gpu = [num_layers_per_gpu] * len(visible_devices)
    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)

    layer_cnt = 0
    for i, num_layer in enumerate(num_layers_per_gpu):
        for j in range(num_layer):
            device_map[f"language_model.model.layers.{layer_cnt}"] = visible_devices[i]
            layer_cnt += 1
    device_map["vision_model"] = visible_devices[0]
    device_map["mlp1"] = visible_devices[0]
    device_map["language_model.model.tok_embeddings"] = visible_devices[0]
    device_map["language_model.model.embed_tokens"] = visible_devices[0]
    device_map["language_model.output"] = visible_devices[0]
    device_map["language_model.model.norm"] = visible_devices[0]
    device_map["language_model.model.rotary_emb"] = visible_devices[0]
    device_map["language_model.lm_head"] = visible_devices[0]
    device_map[f"language_model.model.layers.{num_layers - 1}"] = visible_devices[0]

    return device_map, visible_devices


def split_model_old(model_name):
    import math

    device_map = {}
    num_gpus = torch.cuda.device_count()
    rank, world_size = get_rank_and_world_size()
    num_gpus = num_gpus // world_size

    num_layers_map = {
        "InternVL2-8B": 32,
        "InternVL2-26B": 48,
        "InternVL2-40B": 60,
        "InternVL2-Llama3-76B": 80,
    }

    if model_name not in num_layers_map:
        return "cuda"
    num_layers = num_layers_map[model_name]
    # Since the first GPU will be used for ViT, treat it as 0.5 GPU.
    num_layers_per_gpu = math.ceil(num_layers / (num_gpus - 0.5))
    num_layers_per_gpu = [num_layers_per_gpu] * num_gpus
    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
    layer_cnt = 0
    for i, num_layer in enumerate(num_layers_per_gpu):
        for j in range(num_layer):
            device_map[f"language_model.model.layers.{layer_cnt}"] = (
                rank + world_size * i
            )
            layer_cnt += 1
    device_map["vision_model"] = rank
    device_map["mlp1"] = rank
    device_map["language_model.model.tok_embeddings"] = rank
    device_map["language_model.model.embed_tokens"] = rank
    device_map["language_model.output"] = rank
    device_map["language_model.model.norm"] = rank
    device_map["language_model.lm_head"] = rank
    device_map["language_model.model.rotary_emb"] = rank
    device_map[f"language_model.model.layers.{num_layers - 1}"] = rank
    return device_map


def build_mcq_cot_prompt(line, prompt):
    cot_prompt = (
        "Answer the preceding multiple choice question. The last line of your response should follow "
        "this format: 'Answer: \\boxed{$LETTER}' (without quotes), where LETTER is one of the options. "
        "If you are uncertain or the problem is too complex, make a reasoned guess based on the "
        "information provided. Avoid repeating steps indefinitely—provide your best guess even if "
        "unsure. Think step by step logically, considering all relevant information before answering."
    )
    prompt = prompt.replace(
        "Answer with the option's letter from the given choices directly.", ""
    ).strip()
    prompt = prompt + "\n" + cot_prompt

    return prompt


def build_qa_cot_prompt(line, prompt):
    cot_prompt = (
        "Answer the preceding question. The last line of your response should follow this format: "
        "'Answer: \\boxed{$FINAL_ANSWER}' (without quotes), where 'FINAL_ANSWER' is your conclusion "
        "based on the reasoning provided. If you are uncertain or the problem is too complex, make "
        "a reasoned guess based on the information provided. Avoid repeating steps indefinitely—"
        "provide your best guess even if unsure. Think step by step logically, considering all "
        "relevant information before answering."
    )
    prompt = prompt + "\n" + cot_prompt

    return prompt


def build_multi_choice_prompt(line, dataset=None):
    question = line["question"]
    hint = line["hint"] if ("hint" in line and not pd.isna(line["hint"])) else None
    if hint is not None:
        question = hint + "\n" + question

    options = {
        cand: line[cand]
        for cand in string.ascii_uppercase
        if cand in line and not pd.isna(line[cand])
    }
    for key, item in options.items():
        question += f"\n{key}. {item}"
    prompt = question

    if len(options):
        prompt += (
            "\n请直接回答选项字母。"
            if cn_string(prompt)
            else "\nAnswer with the option's letter from the given choices directly."
        )
    else:
        prompt += (
            "\n请直接回答问题。"
            if cn_string(prompt)
            else "\nAnswer the question directly."
        )

    return prompt


def build_video_prompt(prompt, dataset=None, max_frames=64):
    for start in range(0, max_frames, 8):
        images_to_remove = "".join(
            [f"<Image-{i}>" for i in range(start + 1, start + 9)]
        )
        prompt = prompt.replace(images_to_remove, "")
    for i in range(max_frames):
        prompt = prompt.replace(f"Image-{i + 1}", f"Frame-{i + 1}")
    if listinstr(["MMBench-Video"], dataset):
        prompt = prompt.replace("\nAnswer:", "")
    elif listinstr(["Video-MME"], dataset):
        prompt = prompt.replace("\nAnswer:", "")
        prompt += "\nAnswer with the option's letter from the given choices directly."
    elif listinstr(["MVBench"], dataset):
        prompt = prompt.replace("Best option:(", "")

    return prompt


def reorganize_prompt(message, image_num, dataset=None):
    if dataset is not None and listinstr(["MUIRBench"], dataset):
        prompt = "\n".join([x["value"] for x in message if x["type"] == "text"])
        images_to_remove = " ".join(["<image>"] * image_num)
        prompt = prompt.replace(images_to_remove, "")
        for i in range(image_num):
            prompt = prompt.replace("<image>", f"<Image-{i + 1}>", 1)
        prompt = (
            "".join([f"Image-{i + 1}: <image>\n" for i in range(image_num)]) + prompt
        )
    elif image_num == 1:
        prompt = "<image>\n" + "\n".join(
            [x["value"] for x in message if x["type"] == "text"]
        )
    else:
        prompt, image_idx = "", 1
        for x in message:
            if x["type"] == "text":
                prompt += x["value"]
            elif x["type"] == "image":
                prompt += f"<Image-{image_idx}>"
                image_idx += 1
        prompt = (
            "".join([f"Image-{i + 1}: <image>\n" for i in range(image_num)]) + prompt
        )
        images_to_remove = "".join([f"<Image-{i + 1}>" for i in range(image_num)])
        prompt = prompt.replace(images_to_remove, "")
    return prompt


mpo_prompt_with_final_answer = (
    "Your task is to answer the question below. "
    "Give step by step reasoning before you answer, and when you're ready to answer, "
    'please use the format "Final answer: .."'
    "\n\n"
    "Question:"
    "\n\n"
    "{question}"
)

mpo_prompt_without_final_answer = (
    "Your task is to answer the question below. "
    "Give step by step reasoning. "
    "\n\n"
    "Question:"
    "\n\n"
    "{question}"
)


def mpo_post_processing(response, dataset):

    def extract_answer(text):
        match = re.search(r"(Final answer:|Answer:)\s*(.*)", text, re.IGNORECASE)
        if match:
            return match.group(2).strip()
        return text

    if dataset is not None and (
        DATASET_TYPE(dataset) in ["Y/N", "MCQ"] or listinstr(["CRPE"], dataset)
    ):
        response = extract_answer(response).strip()
    return response


def build_mpo_prompt(message, line, dataset):
    if listinstr(["LLaVABench", "MMVet"], dataset):
        return message

    question_orig = line["question"]
    if listinstr(["MathVerse", "MathVision"], dataset):
        question_orig = question_orig.split("Question:", 1)[-1].strip()
        question_orig = question_orig.replace("Choices:\n", "").strip()
    if listinstr(["WeMath"], dataset):
        question_orig = question_orig.replace(
            "Regarding the format, please answer following the template below, and be sure to include two <> symbols:\n<Thought process>: <<your thought process>> <Answer>: <<your option>>",
            "",
        ).strip()
    options = {
        cand: line[cand]
        for cand in string.ascii_uppercase
        if cand in line and not pd.isna(line[cand])
    }
    options_prompt = ""
    for key, item in options.items():
        options_prompt += f"{key}. {item}\n"

    if options_prompt.strip():
        question_orig = f"{question_orig}\n{options_prompt}"

    cot_prompt = mpo_prompt_with_final_answer
    prompt = cot_prompt.format(question=question_orig).strip()
    message[0]["value"] = prompt
    return message
